Application-security teams
Prioritize likely exploitable findings, review trends across repositories, and apply centralized security policies without handing developers a raw scanner backlog.
Independent tool overview
DryRun Security at a glance
DryRun Security is an AI-native application-security platform that reviews pull requests and repositories with codebase context, prioritizes exploitable risk, and enforces custom policies written in natural language.
Visit the official DryRun Security site ↗.png&w=3840&q=75)
- Best for
- AppSec and engineering teams securing fast-moving codebases
- Core workflow
- Context-aware PR and full-repository security analysis
- Source control
- GitHub and GitLab
- Pricing
- Custom quote based on team size
- Deployment
- SaaS
- Reviewed
- August 30, 2026
Overview
What DryRun Security is
DryRun Security is built for engineering and AppSec teams that want security feedback inside the software-development workflow. It connects to GitHub or GitLab, analyzes pull requests alongside relevant codebase context, and returns focused findings with explanations and remediation guidance. Its Contextual Security Analysis approach is designed to reason about data flow, authorization boundaries, business logic, and exploitability instead of relying only on pattern matching.
The platform combines continuous PR review with DeepScan full-repository analysis, natural-language Custom Code Policies, merge-blocking controls, a central risk register, and programmatic access through an API and MCP integrations. It is a serious team product rather than a lightweight developer utility: pricing is quote-based, source-code access and data handling require security review, and AI-generated findings still need human validation before teams treat them as proven vulnerabilities or safe fixes.
Use cases
Who DryRun Security is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Engineering organizations using AI coding agents
Add an independent security-review layer for human- and AI-generated changes from tools such as Codex, Claude Code, Cursor, and Copilot.
Teams with codebase-specific rules
Turn internal architecture, compliance, and secure-coding requirements into natural-language policies that run on every pull request.
Legacy-application reviews
Use DeepScan to establish a full-repository risk baseline instead of analyzing only changes made after installation.
Developer-first security programs
Return focused comments, checks, and fix guidance where developers already review code, with configurable gates for higher-risk findings.
Capabilities
Core DryRun Security features
Contextual Security Analysis
Builds a model of architecture, routes, authentication, data flow, code relationships, and Git behavior to evaluate findings in application context.
AI-native SAST
Looks for injection, authorization, IDOR, logic, secrets, and other code risks while considering reachability, likelihood, and impact.
Pull-request security reviews
Runs when pull requests are opened or updated and posts code-referenced findings, explanations, and remediation guidance into the review workflow.
DeepScan Agent
Analyzes an entire repository on demand or on a schedule to find accumulated and cross-file risks that a diff-only scan can miss.
Custom Code Policies
Lets teams describe security, architecture, compliance, or process requirements in plain English and enforce up to seven policies per repository.
Configurable merge blocking
Supports severity thresholds plus analyzer- and policy-specific status checks that can be required through source-control branch protection.
Risk register and trends
Centralizes findings, triage state, repository visibility, daily insights, and security trends for engineering and security owners.
Developer and admin permissions
Maps GitHub and GitLab roles into account permissions, limiting developer visibility to repositories they can already access while reserving configuration controls for admins.
API and MCP access
Provides programmatic access to repositories, scans, analyzers, policies, findings, and insights, with integrations for AI coding tools and assistants.
Broad language coverage
The product lists support for major stacks including Python, JavaScript, TypeScript, Java, C#, Ruby, Go, C++, PHP, Kotlin, Swift, Scala, and more.
Infrastructure-as-code scanning
Includes checks for Terraform configurations, with Custom Code Policies available for additional infrastructure requirements.
GitHub, GitLab, and Slack integrations
Connects scanning to source-control workflows and can route notifications and security collaboration into Slack.
Process
How the DryRun Security workflow works
Step 1
Connect source control
Install DryRun Security for the appropriate GitHub or GitLab organization and grant access only to the repositories in scope.
Step 2
Establish a baseline
Run DeepScan on important repositories to identify pre-existing risks that will not appear in future pull-request diffs.
Step 3
Configure PR scanning
Choose repositories, enable the relevant security agents, set notification behavior, and decide which findings should appear in review comments.
Step 4
Add organization-specific policies
Draft and test natural-language policies against recent code, then attach the validated policies to repository configurations.
Step 5
Tune enforcement
Start new checks in silent mode, review false positives and missed context, then add severity-based or policy-specific blocking where confidence is high.
Step 6
Triage and remediate
Have developers and security owners validate findings, apply fixes, and use the risk register, trends, API, or MCP integrations to track follow-through.
Cost
DryRun Security pricing and free plan
DryRun Security does not publish fixed plan prices. Its FAQ says pricing is aligned with the number of developers, security-team members, and owners who need codebase visibility, so organizations need a custom quote and should confirm included repositories, scans, policies, integrations, support, and contract minimums.
Custom team plan
Contact sales
SaaS pricing based on the size of the engineering and security teams using the platform and the owners who require codebase visibility.
- No public per-user or per-repository rate
- Confirm DeepScan usage and limits
- Confirm repository and policy allowances
- Request security, compliance, support, and data-retention terms
Pricing checked . Check current pricing at the source ↗
Assessment
DryRun Security strengths and limitations
Where it stands out
- Combines pull-request scanning and full-repository analysis in one security workflow.
- Uses surrounding codebase, data-flow, authorization, and business-logic context to prioritize findings.
- Natural-language policies can encode organization-specific rules without a dedicated query language.
- Feedback appears directly in GitHub or GitLab with code references and remediation guidance.
- Configurable silent mode and blocking controls support a gradual rollout instead of an immediate hard gate.
- Risk register, trends, API access, and MCP integrations extend the product beyond one-off PR comments.
- DeepScan can surface risks that predate installation or span multiple files and changes.
- Role mapping and repository-scoped visibility provide useful administrative separation for larger teams.
What to consider
- There is no public price list, so buyers cannot estimate total cost without a sales process.
- It is designed for teams and security programs, not an instant self-serve scanner for a single casual project.
- The SaaS service needs access to sensitive source code and repository metadata, which requires vendor, privacy, retention, and access-control review.
- The company describes strong accuracy and noise-reduction results, but buyers should validate those claims on their own languages, frameworks, and vulnerability mix.
- AI-assisted analysis is probabilistic; findings can still be false positives, miss vulnerabilities, or suggest incomplete remediations.
- Static analysis cannot prove that an application is secure and does not replace runtime testing, dependency governance, penetration testing, threat modeling, or human review.
- Custom policies depend on clear context and testing; vague natural-language requirements can produce inconsistent or overly broad results.
- Blocking checks can slow delivery if teams enforce them before tuning thresholds and validating signal quality.
- DeepScan can take hours according to the product documentation, so it is a baseline and periodic-review tool rather than immediate feedback.
- The documented Custom Code Policy limit is seven attached policies per repository, which may constrain organizations with many granular controls.
- Primary source-control support centers on GitHub and GitLab; teams using other hosts should confirm current compatibility.
- The product's public compliance language describes generating SDLC-control artifacts; that does not by itself make a customer compliant with SOC 2, ISO 27001, PCI, or HIPAA.
Compare
DryRun Security alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Agents
Codex Security
Consider Codex Security if you want OpenAI's repository threat modeling, vulnerability validation, and patch proposals and already qualify for its research preview.
Explore Codex Security →Coding
Claude Security
Consider Claude Security if your team is centered on Anthropic and wants scheduled repository scans, patch generation, and security-workflow exports.
Explore Claude Security →Coding
Claude Code Review
Consider Claude Code Review when the broader need is multi-agent pull-request review rather than a dedicated AppSec platform with policy enforcement and a risk register.
Explore Claude Code Review →Questions
DryRun Security FAQs
What is DryRun Security?
DryRun Security is an AI-native application-security platform for contextual pull-request reviews, full-repository scans, custom code policies, risk tracking, and remediation guidance across human- and AI-generated code.
How much does DryRun Security cost?
DryRun Security does not publish fixed prices. Its FAQ says pricing is aligned with the number of developers, security-team members, and owners using the platform, so buyers need a custom quote.
Does DryRun Security work with GitHub and GitLab?
Yes. Official product and documentation pages describe GitHub and GitLab integrations for installation, pull-request analysis, role mapping, comments, and status checks.
What is Contextual Security Analysis?
It is DryRun Security's approach to evaluating code with surrounding architecture, data flow, authorization, Git history, frameworks, and application behavior so findings can be prioritized by likely exploitability and impact.
What is the difference between PR Review and DeepScan?
PR Review analyzes incoming code changes and provides fast feedback in the pull request. DeepScan analyzes the complete repository to find existing or cross-cutting risks that may not appear in one diff.
Can DryRun Security block a pull request?
Yes. Teams can configure a severity threshold or specific analyzer and policy checks, then require the corresponding DryRun status checks through branch-protection rules.
Can teams write their own security rules?
Yes. Custom Code Policies express requirements in natural language, can be tested before rollout, and can run on every pull request. Current documentation says up to seven policies can be attached per repository.
Does DryRun Security store source code?
The vendor says it analyzes repositories and stores contextual markers rather than source code, and that access remains controlled through GitHub or GitLab. Organizations should still verify current data flows, subprocessors, retention, model usage, and contract terms during security review.
Can DryRun Security replace penetration testing or human AppSec review?
No. It can improve code-review coverage and prioritization, but static and AI-assisted analysis cannot prove an application is secure. High-risk systems still need layered testing, threat modeling, dependency controls, runtime monitoring, and expert review.
Bottom line
Our DryRun Security verdict
DryRun Security is most compelling for AppSec teams that need contextual security review embedded in pull requests, plus repository-wide baselines and organization-specific policy enforcement. The combination of DeepScan, natural-language policies, merge controls, and centralized risk visibility is broader than a simple AI reviewer. The purchasing decision should hinge on a proof of value using representative repositories: measure true-positive rate, missed issues, review latency, developer acceptance, and total quoted cost before allowing the system to block merges.
Visit DryRun Security website ↗Similar Coding Tools
View all Coding AI tools →
AI training for the future of work.
Get access to all our AI courses, hundreds of real-world AI use cases, live expert-led workshops, an exclusive network of AI early adopters, and more.
AI Courses
Get unlimited access to all of our current & upcoming industry-specific AI courses for the duration of your subscription.
Daily Guides
To keep up with the rapid pace of AI, our team publishes AI implementation guides daily. Our library contains 300+ practical use cases to automate real-world work.
Workshops
Join weekly, live, interactive sessions with industry leaders who are at the forefront of AI for hands-on implementation guidance and exclusive insights.
Community
Network with an exclusive community of AI-first professionals who are working smarter with AI. Learn how early adopters are using AI in their work and businesses.